This article was first published in the Journal of Performance Measurement Volume 27-4, Summer 2023
Part 1 of this article was the first known attempt to categorise and compare across implemented FIA models using a common dataset.
The objective was to see if a ‘Generic’ Model could be defined for FIA in the same way that Brinson has become one for Equity Attribution.
No ‘Generic Model’ exists for Fixed Income Attribution. Instead, different academics working alone or with software suppliers have, historically, individually designed models.
Part 1 identified significant commonality as well as differences across the four models compared. Two years on, Part 2 adds a further three models to the analysis, seeking through the increased number to confirm the Part 1 commonality identified and, with the passage of time, detect any new commonality and/or reporting trends.
INTRODUCTION
Until release of the 2020 Performance Standards, GIPS compliance restricted Firms to the Modified Dietz/Time Weighted algorithm for Performance Reporting. The 2020 flexibility, section 1.A.35, additionally allowed Money Weighted returns where firms have control of external cash flows.
Whilst not part of GIPS, Equity Attribution enjoys near-global algorithm standardisation via Brinson Additive. Significant flexibility has, however, arrived since its 1985 introduction. The ‘Bottom Up’ alternative to the original ‘Top Down’ approach is one example. We also see the optional geometric algorithms, different approaches to the treatment of non-Benchmark securities and different multi-period smoothing options.
Fixed Income Attribution, with us now for over 30 years, offers near-total flexibility, being viewed as a series of independent models lacking standards or even, apparently, commonality. Accordingly, FIA implementations, recently termed a ‘non standardised endeavour’ (Aite-Novarica, 2021), offer significant configurability in order to meet user requirements, see further below.
Part 1 of this article considered the options for greater standardisation via a Brison-like ‘Generic Model’. As the first stage in such a journey it sought cross-model commonality.
Part 1’s method was to calculate and compare the returns produced by different models from a single set of data. This journal’s FIA model articles to date and the model descriptions in the Public Domain have used independent data sets thus complicating direct comparison of commonality.
Part 1 scope included the:
Commonality was identified across all four models in the ‘Income’ and ‘Price’ (ie Government + Corporate Term Structures) return categories even though the levels of analysis and the algorithms employed under each of these varied. Income Return, for example, was defined by different models as any of Current Yield, Yield to Maturity and Accrued Income Yield. Existence of other return categories, for example Currency, Swap Curve, Instrument Specific, varied by model.
Part 1 also identified the existence of three FIA categories - ‘Bottom Up’, ‘Top Down’ and ‘Hybrid’. The models compared offered anything from one to all three of these approaches, the ‘Hybrid’ case comprising two separate reports, each independently reconciling to the Active Return.
PART 2 – INITATIVE
Post Part 1, consideration was given to enhancing its observations via further model coverage.
This required:
Aite-Novarica’s 2021 ‘FIA best in class Matrix’ publication was used as the start point for (a). Of the thirteen models/suppliers listed in the Matrix, two had already been covered in Part 1. Of the remainder, ten of the suppliers were contacted with view to participation in Part 2. Of these, three kindly agreed to become involved in the production of returns from the same Part 1 dataset, for which the author is most grateful. Some calculated returns themselves, others provided algorithms.
Whilst not the most statistical approach to selection, it has proved useful all the same.
PART 2 – APPROACH
Data
The following dataset enhancements were introduced for Part 2:
Models Compared
The returns of the three new models were compared to each other and to those of the two most relevant Part 1 models (Figure 4). The chosen Part 1 models were the ‘Extended Bottom Up’ (£ base) and Flametree Hybrid (US$ base).
Model Categories
The model categories identified in Part 1, Bottom-Up (BU), Top-Down (TD) and Hybrid (ie includes both BU and TD characteristics) persist in Part 2 although an alternative definition of Hybrid also became apparent.
The following similarities/differences in comparison to Brinson/Equity Attribution, from which the TU, BU and Hybrid terms have been adopted, were identified:
Similarities
Differences
TD, however, is always benchmark-relative.
Model Configuration
FIA’s quoted ‘non-standard endeavour’ partly provides the flexibility required to meet varying user requirements within the complex discipline of Fixed Income Funds Management.
FIA moves away from the limited flexibility applying to Performance Reporting/Equity Attribution systems, reasons including the following:
This complexity of approach means that returns (Figure 4) will not exactly reconcile across models.
Comparing Government Curve Parallel Return, for example. Figure 3 shows that some model versions used have based this on 5-year Tenor δY, others on Curve Average δY (choice often configurable). This will give return differences. Further, with data, the practical result of analysing the Part 1 Portfolio – level returns into Part 2 Sector-level returns has introduced small differences.
With production, some of the returns have been produced via the daily calculations of the systems themselves, others as a single quarterly spreadsheet-based return. This also will give small variations in returns.
Given this, a number of the cross-model returns in Figure 4 are remarkably close, giving support to theory of commonality.
Aside from specific returns, the Figure 4 output usefully shows the Return Categories available across all models, both Core (all models) and Non-Core (some models only) Returns.
PART 2 – OUTPUT SPECIFICS
Return Mechanics
The first reporting step is to confirm the Performance Return (Portfolio Contribution or Active Return) start point.
BU attribution calculations normally follow next with TD attribution where offered as the last step.
Return Algorithms
Figure 3 below gives an indication of the algorithms used across models in both Parts 1 and 2.
Commonality/Bottom Up and Top Down
Considerable commonality persists across BU, especially across the ‘Core Returns’ of Part 1.
With TD, the ‘Full Hybrid’ Flametree model of Part 1 wasn’t replicated by the Part 2 models. It doesn’t mean Full Hybrid lacks validity, just that the relevant (different) models were not offered as part of Part 2. For example, while the particular Bloomberg PORT model used showed a different Hybrid approach (a combined set of BU/TD returns, Figure 2), this was only one model option and other model types exist which can replicate the Flametree approach.
Sector Weightings/Instrument Classifications
Figure 4 shows common Portfolio and Benchmark Sector Weightings across four of the five models. The slightly different Bloomberg PORT weightings arise because one security qualified for a shorter TTM during the reporting period and the system correctly reflected that.
Instrument Configuration
For instrument types beyond bonds, Fusion Invest, for example, offers a pricing library and toolbox, allowing managers to user-define valuations and FIA return.
CONCLUSIONS
Bearing in mind the limited number of models compared thus far across both Parts of this article:
References
‘Aite Matrix: Fixed Income Attribution and Analytics’ Paul Sinthunont and Audrey Blatter, Aite-Novarica, April 2021
‘Decision Driven Fixed Income Attribution’ Pam Zhong, October 2012
I am most grateful to Bas Leerink of Ortec Finance and to Fabien Trigano of Finastra Fusion for their assistance with this article and to Ian Thompson, Carl Bacon and Tim Escott for their peer reviews and general assistance. Any errors remain entirely my own responsibility.
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